\begin{table}[t]
\centering
\caption{\textbf{Effect of Voter ID Law on Primary Election Turnout Among Those Without ID, Individual Level, 2008--2018.}
\label{tab:voter_id_primary_2018}}
\resizebox{\textwidth}{!} {
\begin{tabular}{lccccccc}
\toprule \toprule
 & \multicolumn{7}{c}{Voted in Primary (0-1)}\\
 & (1) & (2) & (3) & (4) & (5) & (6) & (7) \\
\midrule
No DMV Match * Year $\geq$ 2016 & -0.078 & -0.073 & -0.054 & -0.048\\
 & (0.001) & (0.001) & (0.001) & (0.001)  \smallskip\\
No DMV Match * Year $\geq$ 2018 & 0.102 & 0.087 & 0.088 & 0.077 & -0.003 & -0.003 & -0.001\\
 & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) \smallskip\\
 N &   39,763,872 &   39,763,872 &   39,707,406 &   39,707,382 &   33,136,560 &   33,136,560 &   33,089,505 \\ 
 \# Voters &    6,627,312 &    6,627,312 &    6,617,901 &    6,617,897 &    6,627,312 &    6,627,312 &    6,617,901 \\ 
Individual FEs & Y & Y & Y & Y & N & N & N \\
Year FEs & Y & N & N & N & Y & Y & Y  \\
Race by Year FEs & N & Y & N & N & N & N & N  \\
Age by Year FEs & N & N & Y & N & N & N & N \\
Race by Age by Year FEs & N & N & N & Y & N & N & N \\
Exact Match on Turnout & N & N & N & N & Y & Y & Y \\
Exact Match on Race & N & N & N & N & N & Y & Y \\
Exact Match on Age Bin & N & N & N & N & N & N & Y \\
\bottomrule \bottomrule
\multicolumn{8}{p{1.15\textwidth}}{\footnotesize Robust standard errors clustered by individual in parentheses.  Main effects for No DMV Match and 2016 are absorbed by fixed effects.  
Exact matching on turnout matches units based on each primary and general election from the 2008 primary through the 2014 general.  For exact matching on age, we construct a separate age bin for 
each group of voters who were under 18 for a given set of elections, so the cohort of voters who became newly eligible to participate in 2010, 2012, 2014, and 2016 each have their own age bin.  
For voters who were eligible for all elections since 2008, we construct age deciles.}
\end{tabular}}
\end{table}
